Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design anddemand of quality products. To make decision making process online, effective and efficient artificial intelligent tools likeneural networks are being attempted. This paper proposes the development of neural network models for prediction ofweld quality in Submerged Arc Welding (SAW). Experiments are designed according to Taguchi’s principles andmathematical equations are developed using multiple regression model. Proposed neural network models are developedusing experimental data, supported with the data generated by regression model. The performances of the developedmodels are compared in terms of computational speed and prediction accuracy. It is found that Neural Network trainedwith Particle Swarm Optimization (NNPSO) performs better than Neural Network trained with Back Propagation (BPNN)algorithm, Radial Basis Functional Neural Network (RBFNN) and Neural Network trained with Genetic Algorithm(NNGA). The developed scheme for weld quality prediction is flexible, competent, and accurate than existing models andit scopes better online monitoring system. Finally the developed models are validated. The proposed and developedtechnique finds a good scope and a better future in the relevant field where human can avoid unwanted risks duringoperations with the deployment of robots.
The structural material identified for this work is 15CDV6 steel belongs to the category of High strength low alloy (HSLA) family. This research is conducted in the area of Powdered flux TIG (PFTIG) and Flux bounded TIG (FBTIG) welding with the assistance of optimum process parameters procured from Grey wolf optimization tool generated in MATLAB software. The optimum nanopowder and flux gap obtained from PFTIG and FBTIG welding are utilized for the fabrication of closed square butt joint. The microstructural characterization of FBTIG weldments is analyzed in Post weld heat treated (PWHT) condition. The microstructural examinations were conducted with the help of optical and scanning electron microscopy (SEM) to evaluate the structures in the weld zone, Heat affected zone and parent material. The presence of coarse grain structure observed in the WZ leads to the lower strength and hardness of the weldment. The presence of tempered martensite in the HAZ induces strength and toughness to the region.
Manufacturing and maintaining different aircraft fleet leads to various purposes, which consumes more money as well as man power. Solution to this, nations that are leading in the field of aeronautics are performing much research and development works on new aircraft designs that could do the operations those were done by varied aircrafts. The foremost benefit of this delta wing is, along the huge rearward sweep angle, the wing’s leading edge would not contact the boundary of shock wave. Further, the boundary is produced at the fuselage nose due to the speed of aircraft approaches and also goes beyond the transonic to supersonic speed. Further, rearward sweep angle greatly worse the airspeed: wings under normal condition to leading edge, so permits the aircraft to fly at great transonic, subsonic, or supersonic speed, whereas the over wing speed is kept to minimal range than that of the sound speed. The cropped delta wing with fence has analysed in three cases: Fences at 3/4th distance from the centre, with fences at half distance from the centre and with fences at the centre. Further, the delta wing that cropped is exported to ANSYS FLUENT V14.0 software and analysed by making the boundary condition settings like sonic Mach number of flow over wing along with the angle of attack.
Flux cored arc welding has been applied in manufacturing industries for more than fifteen years. The quality of weld mainly depends on the mechanical properties of the weld, which in turn relays on the interaction of the weld parameters. This paper discusses the multi response optimization of weld parameters using grey based Taguchi method. Grey relational analysis was carried out to convert multi objective criterion into equivalent single objective function; overall grey relational grade, which is optimized by the Taguchi technique. Experiments are conducted using Taguchi's L27 orthogonal array. The weld parameters used in this study were welding current, welding speed, and arc voltage with bead hardness and material deposition rate as responses. Taguchi's Signal-to-Noise (S/N) ratio is computed based on their performance characteristics. Grey relational grade was obtained using Signal-toNoise ratio values of responses. Based on the grey relational grade, optimum levels of parameters have been identified. Significant contributions were estimated using Analysis of Variance (ANOVA). A confirmation test was conducted to validate the proposed method. This evaluation procedure could be used in decision-making to select process parameters for a welding operator. The proposed and developed method has good accuracy and competency with the predicted value enhancing automation and robotization.
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